Long-term air pollution exposure impact on COVID-19 morbidity in China

Wu, Y., Zhan, Q. and Zhao, Q. (2021) Long-term air pollution exposure impact on COVID-19 morbidity in China. Aerosol and Air Quality Research, 21(1), 200413. (doi: 10.4209/aaqr.2020.07.0413)

[img] Text
223121.pdf - Published Version
Available under License Creative Commons Attribution.



Although previous studies have proved the association between air pollution and respiratory viral infection, given the relatively short history of human infection with the severe acute respiratory syndrome coronavirus (SARS-CoV-2), the linkage between long-term air pollution exposure and the morbidity of 2019 novel coronavirus (COVID-19) pneumonia remains poorly understood. To fill this gap, this study investigates the influences of particulate matters (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2) and carbon monoxide (CO) on COVID-19 incidence rate based on the prefecture-level morbidity count and air quality data in China. Annual means for ambient PM2.5, PM10, SO2, NO2, CO and O3 concentrations in each prefecture are used to estimate the population’s exposure. We leverage identical statistical methods, i.e., Spearman’s rank correlation and negative binomial regression model, to demonstrate that people who are chronically exposed to ambient air pollution are more likely to be infected by COVID-19. Our statistical analysis indicates that a 1 μg m-3 increase of PM2.5, PM10, NO2 and O3 can result in 1.95% (95% CI: 0.83 to 3.08% ), 0.55% (95% CI: -0.05 to 1.17% ), 4.63% (95% CI: 3.07 to 6.22% ) rise and 2.05% (95% CI: 0.51 to 3.59 % ) decrease of COVID-19 morbidity. However, we observe nonsignificant association with long-term SO2 and CO exposure to COVID-19 morbidity in this study. Our results’ robustness is examined based on sensitivity analyses that adjust for a wide range of confounders, including socio-economic, demographic, weather, healthcare, and mobility-related variables. We acknowledge that more laboratory results are required to prove the etiology of these associations.

Item Type:Articles
Keywords:Air pollution exposure, COVID-19 morbidity, prefecture-level data, negative binomial regression.
Glasgow Author(s) Enlighten ID:Zhao, Dr Qunshan
Authors: Wu, Y., Zhan, Q., and Zhao, Q.
College/School:College of Social Sciences > School of Social and Political Sciences > Urban Studies
Journal Name:Aerosol and Air Quality Research
Publisher:Taiwan Association for Aerosol Research
ISSN (Online):2071-1409
Published Online:04 September 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in Aerosol and Air Quality Research 21(1): 200413
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
190698Urban Big Data Research CentreNick BaileyEconomic and Social Research Council (ESRC)ES/L011921/1S&PS - Urban Big Data
304042UBDC Centre TransitionNick BaileyEconomic and Social Research Council (ESRC)ES/S007105/1S&PS - Administration